An, Jiyuan and Chen, Yi-Ping Phoebe 2005, Keyword extraction for text categorization, in Proceedings of the 2005 International Conference on Active Media Technology, IEEE, Piscataway, N.J., pp. 556-561.
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Proceedings of the 2005 International Conference on Active Media Technology
Tarumi, H. Li, Y. Yoshida, T.
Active Media Technology Conference
Place of publication
Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.
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Field of Research
080199 Artificial Intelligence and Image Processing not elsewhere classified
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